Predictive modeling for postoperative delirium in elderly patients with abdominal malignancies using synthetic minority oversampling technique

BACKGROUND Postoperative delirium,particularly prevalent in elderly patients after abdominal cancer surgery,presents significant challenges in clinical management.AIM To develop a synthetic minority oversampling technique(SMOTE)-based model for predicting postoperative delirium in elderly abdominal cancer patients.METHODS In this retrospective cohort study,we analyzed data from 611 elderly patients who underwent abdominal malignant tumor surgery at our hospital between September 2020 and October 2022.The incidence of postoperative delirium was recorded for 7 d post-surgery.Patients were divided into delirium and non-delirium groups based on the occurrence of postoperative delirium or not.A multivariate logistic regression model was used to identify risk factors and develop a predictive model for postoperative delirium.The SMOTE technique was applied to enhance the model by oversampling the delirium cases.The model’s predictive accuracy was then validated.RESULTS In our study involving 611 elderly patients with abdominal malignant tumors,multivariate logistic regression analysis identified significant risk factors for postoperative delirium.These included the Charlson comorbidity index,American Society of Anesthesiologists classification,history of cerebrovascular disease,surgical duration,perioperative blood transfusion,and postoperative pain score.The incidence rate of postoperative delirium in our study was 22.91%.The original predictive model(P1)exhibited an area under the receiver operating characteristic curve of 0.862.In comparison,the SMOTE-based logistic early warning model(P2),which utilized the SMOTE oversampling algorithm,showed a slightly lower but comparable area under the curve of 0.856,suggesting no significant difference in performance between the two predictive approaches.CONCLUSION This study confirms that the SMOTE-enhanced predictive model for postoperative delirium in elderly abdominal tumor patients shows performance equivalent to that of traditional methods,effectively addressing data imbalance.

Clinical nursing value of predictive nursing in reducing complications of pregnant women undergoing short-term massive blood transfusion during cesarean section

BACKGROUND Cesarean hemorrhage is one of the serious complications,and short-term massive blood transfusion can easily cause postoperative infection and physical stress response.However,predictive nursing intervention has important clinical significance for it.AIM To explore the effect of predictive nursing intervention on the stress response and complications of women undergoing short-term mass blood transfusion during cesarean section(CS).METHODS A clinical medical record of 100 pregnant women undergoing rapid mass blood transfusion during sections from June 2019 to June 2021.According to the different nursing methods,patients divided into control group(n=50)and observation group(n=50).Among them,the control group implemented routine nursing,and the observation group implemented predictive nursing intervention based on the control group.Moreover,compared the differences in stress res-ponse,complications,and pain scores before and after the nursing of pregnant women undergoing rapid mass blood transfusion during CS.RESULTS The anxiety and depression scores of pregnant women in the two groups were significantly improved after nursing,and the psychological stress response of the observation group was significantly lower than that of the control group(P<0.05).The heart rate and mean arterial pressure(MAP)of the observation group during delivery were lower than those of the control group,and the MAP at the end of delivery was lower than that of the control group(P<0.05).Moreover,different pain scores improved significantly in both groups,with the observation group considerably less than the control group(P<0.05).After nursing,complications such as skin rash,urinary retention,chills,diarrhea,and anaphylactic shock in the observation group were 18%,which significantly higher than in the control group(4%)(P<0.05).CONCLUSION Predictive nursing intervention can effectively relieve the pain,reduce the incidence of complications,improve mood and stress response,and serve as a reference value for the nursing of women undergoing rapid mass transfusion during CS.

Hybrid Dynamic Variables-Dependent Event-Triggered Fuzzy Model Predictive Control

This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative dynamic variable and an additive dynamic variable.The addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem(OP).To facilitate the co-design of the MPC controller and the weighting matrix of the DETM,an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant(RPI) set that contain the membership functions and the hybrid dynamic variables.A dynamic event-triggered fuzzy MPC algorithm is developed accordingly,whose recursive feasibility is analysed by employing the RPI set.With the designed controller,the involved fuzzy system is ensured to be asymptotically stable.Two examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.

Real-Time Co-optimization of Gear Shifting and Engine Torque for Predictive Cruise Control of Heavy-Duty Trucks

Fuel consumption is one of the main concerns for heavy-duty trucks.Predictive cruise control(PCC)provides an intriguing opportunity to reduce fuel consumption by using the upcoming road information.In this study,a real-time implementable PCC,which simultaneously optimizes engine torque and gear shifting,is proposed for heavy-duty trucks.To minimize fuel consumption,the problem of the PCC is formulated as a nonlinear model predictive control(MPC),in which the upcoming road elevation information is used.Finding the solution of the nonlinear MPC is time consuming;thus,a real-time implementable solver is developed based on Pontryagin’s maximum principle and indirect shooting method.Dynamic programming(DP)algorithm,as a global optimization algorithm,is used as a performance benchmark for the proposed solver.Simulation,hardware-in-the-loop and real-truck experiments are conducted to verify the performance of the proposed controller.The results demonstrate that the MPC-based solution performs nearly as well as the DP-based solution,with less than 1%deviation for testing roads.Moreover,the proposed co-optimization controller is implementable in a real-truck,and the proposed MPC-based PCC algorithm achieves a fuel-saving rate of 7.9%without compromising the truck’s travel time.

A Novel Predictive Model for Edge Computing Resource Scheduling Based on Deep Neural Network

Currently,applications accessing remote computing resources through cloud data centers is the main mode of operation,but this mode of operation greatly increases communication latency and reduces overall quality of service(QoS)and quality of experience(QoE).Edge computing technology extends cloud service functionality to the edge of the mobile network,closer to the task execution end,and can effectivelymitigate the communication latency problem.However,the massive and heterogeneous nature of servers in edge computing systems brings new challenges to task scheduling and resource management,and the booming development of artificial neural networks provides us withmore powerfulmethods to alleviate this limitation.Therefore,in this paper,we proposed a time series forecasting model incorporating Conv1D,LSTM and GRU for edge computing device resource scheduling,trained and tested the forecasting model using a small self-built dataset,and achieved competitive experimental results.

Predictive Factors for Pre-Eclampsia: A Case-Control Study in Two Hospitals in Yaounde

Introduction: Pre-eclampsia is a major cause of maternal and prenatal morbidity and mortality, that complicates 2% to 8% of pregnancies worldwide. The aim of this study was to determine the predictive factors for pre-eclampsia in two hospitals in the city of Yaoundé. Methods: A case-control study was conducted at the Gynaecology & Obstetrics department of the Yaoundé Gynaeco-Obstetric and Paediatric Hospital (YGOPH) and the Main Maternity of the Yaoundé Central Hospital (MM-YCH) from February 1 to July 30, 2022. The cases were all pregnant women presenting with pre-eclampsia. The control group included pregnant women without pre-eclampsia. Descriptive statistics followed by logistic regression analyses were conducted with level of significance set at p-value Results: Included in the study were 33 cases and 132 controls, giving a total of 165 participants. The predictive factors for pre-eclampsia after multivariate analysis were: primiparity (aOR = 51.86, 95% CI: 3.01 - 1230.96, p = 0.045), duration of exposure to partner’s sperm Conclusion: The odds of pre-eclampsia increased with primiparity, duration of exposure to partner’s sperm < 3 months, personal history of pre-eclampsia and maternal history of pre-eclampsia. Recognition of these predictor factors would improve the ability to diagnose and monitor women likely to develop pre-eclampsia before the onset of disease for timely interventions.

Predictive factors for percutaneous nephrolithotomy bleeding risks

Objective:This study aimed to identify predictive factors for percutaneous nephrolithotomy(PCNL)bleeding risks.With better risk stratification,bleeding in high-risk patient can be anticipated and facilitates early identification.Methods:A prospective observational study of PCNL performed at our institution was done.All adults with radio-opaque renal stones planned for PCNL were included except those with coagulopathy,planned for additional procedures.Factors including gender,co-morbidities,body mass index,stone burden,puncture site,tract dilatation size,operative position,surgeon's seniority,and operative duration were studied using stepwise multivariate regression analysis to identify the predictive factors associated with higher estimated hemoglobin(Hb)deficiency.Results:Overall,4.86%patients(n=7)received packed cells transfusion.The mean estimated Hb deficiency was 1.3(range 0-6.5)g/dL and the median was 1.0 g/dL.Stepwise multivariate regression analysis revealed that absence of hypertension(p=0.024),puncture site(p=0.027),and operative duration(p=0.023)were significantly associated with higher estimated Hb deficiency.However,the effect sizes are rather small with partial eta-squared of 0.037,0.066,and 0.038,respectively.Observed power obtained was 0.621,0.722,and 0.625,respectively.Other factors studied did not correlate with Hb difference.Conclusion:Hypertension,puncture site,and operative duration have significant impact on estimated Hb deficiency during PCNL.However,the effect size is rather small despite adequate study power obtained.Nonetheless,operative position(supine or prone),puncture number,or tract dilatation size did not correlate with Hb difference.The mainstay of reducing bleeding in PCNL is still meticulous operative technique.Our study findings also suggest that PCNL can be safely done by urology trainees under supervision in suitably selected patient,without increasing risk of bleeding.

Expression level of myocardial enzymes in patients with schizophrenia: Predictive value in the occurrence of violence

BACKGROUND Schizophrenic patients are prone to violence,frequent recurrence,and difficult to predict.Emotional and behavioral abnormalities during the onset of the disease,resulting in active myocardial enzyme spectrum.AIM To explored the expression level of myocardial enzymes in patients with schizo-phrenia and its predictive value in the occurrence of violence.METHODS A total of 288 patients with schizophrenia in our hospital from February 2023 to January 2024 were selected as the research object,and 100 healthy people were selected as the control group.Participants’information,clinical data,and labo-ratory examination data were collected.According to Modified Overt Aggression Scale score,patients were further divided into the violent(123 cases)and non-violent group(165 cases).RESULTS The comparative analysis revealed significant differences in serum myocardial enzyme levels between patients with schizophrenia and healthy individuals.In the schizophrenia group,the violent and non-violent groups also exhibited different levels of serum myocardial enzymes.The levels of myocardial enzymes in the non-violent group were lower than those in the violent group,and the patients in the latter also displayed aggressive behavior in the past.CONCLUSION Previous aggressive behavior and the level of myocardial enzymes are of great significance for the diagnosis and prognosis analysis of violent behavior in patients with schizophrenia.By detecting changes in these indicators,we can gain a more comprehensive understanding of a patient’s condition and treatment.

THAPE: A Tunable Hybrid Associative Predictive Engine Approach for Enhancing Rule Interpretability in Association Rule Learning for the Retail Sector

Association rule learning(ARL)is a widely used technique for discovering relationships within datasets.However,it often generates excessive irrelevant or ambiguous rules.Therefore,post-processing is crucial not only for removing irrelevant or redundant rules but also for uncovering hidden associations that impact other factors.Recently,several post-processing methods have been proposed,each with its own strengths and weaknesses.In this paper,we propose THAPE(Tunable Hybrid Associative Predictive Engine),which combines descriptive and predictive techniques.By leveraging both techniques,our aim is to enhance the quality of analyzing generated rules.This includes removing irrelevant or redundant rules,uncovering interesting and useful rules,exploring hidden association rules that may affect other factors,and providing backtracking ability for a given product.The proposed approach offers a tailored method that suits specific goals for retailers,enabling them to gain a better understanding of customer behavior based on factual transactions in the target market.We applied THAPE to a real dataset as a case study in this paper to demonstrate its effectiveness.Through this application,we successfully mined a concise set of highly interesting and useful association rules.Out of the 11,265 rules generated,we identified 125 rules that are particularly relevant to the business context.These identified rules significantly improve the interpretability and usefulness of association rules for decision-making purposes.

Predictive Factors of Renal Damage during Sickle Cell Disease at the Hematology-Oncology Department of Donka University Hospital

Introduction: Sickle cell disease, which is the most common hereditary hemoglobinopathy in the world, attacks all body systems, particularly the kidneys. The view of this study was to investigate the predictive factors of kidney damage during sickle cell disease. Materials and methods: It was a retrospective, descriptive and analytical study on files of sickle cell patients hospitalized in the Hematology-Oncology Department of Donka University Hospital during a period from January 1, 2016 to December 31, 2019. Records of sickle cell patients with one or more renal abnormalities were retained. Sickle cell patients without kidney damage were also selected for a comparative study. Only patients without sickle cell disease were excluded. Results: Seventy-five (75) medical records were collected during the study period. From these cases, thirteen (13) records with kidney disease were observed, a frequency of 17%. The mean age of patients was 24.2 years for extremes of 10 and 65 years. The sex ratio was 1.6 in favor of men. The SSFA2 form was the most represented with 92%. 24-hour proteinuria was measured in 13 patients between whom 6 patients (46.2%) had a proteinuria level ≤ 1 g. Eight (8) patients (61.5%) were in stage 1 of chronic kidney disease. The most common type of renal involvement was tubulo-interstitial nephropathy with 8 patients (61.5%). Bivariate analysis showed that elevated serum creatinine (P 2 form of the sickness (P Conclusion: After the observation of an increased serum creatinine and urea, a predominance observation of the SSFA2 form, it should be possible to target patients for whom screening for kidney damage should henceforth be systematic.

Application of Predictive Model for Efficient Cassava (Manihot esculenta Crantz) Yield in the Face of Climate Variability in Enugu State, Nigeria

Climate variability as occasioned by conditions such as extreme rainfall and temperature, rainfall cessation, and irregular temperatures has considerable impact on crop yield and food security. This study develops a predictive model for cassava yield (Manihot esculenta Crantz) amidst climate variability in rainfed zone of Enugu State, Nigeria. This study utilized data of climate variables and tonnage of cassava yield spanning from 1971 to 2012;as well as information from a questionnaire and focus group discussion from farmers across two seasons in 2023 respectively. Regression analysis was employed to develop the predictive model equation for seasonal climate variability and cassava yield. The rainfall and temperature anomalies, decadal change in trend of cassava yield and opinion of farmers on changes in rainfall season were also computed in the study. The result shows the following relationship between cassava and all the climatic variables: R2 = 0.939;P = 0.00514;Cassava and key climatic variables: R2 = 0.560;P = 0.007. The result implies that seasonal rainfall, temperature, relative humidity, sunshine hours and radiation parameters are key climatic variables in cassava production. This is supported by computed rainfall and temperature anomalies which range from −478.5 to 517.8 mm as well as −1.2˚C to 2.3˚C over the years. The questionnaire and focus group identified that farmers experienced at one time or another, late onset of rain, early onset of rain or rainfall cessation over the years. The farmers are not particularly sure of rainfall and temperature characteristics at any point in time. The implication of the result of this study is that rainfall and temperature parameters determine the farming season and quantity of productivity. Hence, there is urgent need to address the situation through effective and quality weather forecasting network which will help stem food insecurity in the study area and Nigeria at large. The study made recommendations such as a comprehensive early warning system on climate variability incidence which can be communicated to local farmers by agro-meteorological extension officers, research on crops that can grow with little or no rain, planning irrigation scheme, and improving tree planting culture in the study area.

Predictive model for postpartum hemorrhage requiring hysterectomy in a minority ethnic region

BACKGROUND Postpartum hemorrhage(PPH)is a leading cause of maternal mortality,and hysterectomy is an important intervention for managing intractable PPH.Accurately predicting the need for hysterectomy and taking proactive emergency measures is crucial for reducing mortality rates.AIM To develop a risk prediction model for PPH requiring hysterectomy in the ethnic minority regions of Qiandongnan,China,to help guide clinical decision-making.METHODS The study included 23490 patients,with 1050 having experienced PPH and 74 who underwent hysterectomies.The independent risk factors closely associated with the necessity for hysterectomy were analyzed to construct a risk prediction model,and its predictive efficacy was subsequently evaluated.RESULTS The proportion of hysterectomies among the included patients was 0.32%(74/23490),representing 7.05%(74/1050)of PPH cases.The number of deliveries,history of cesarean section,placenta previa,uterine atony,and placenta accreta were identified in this population as independent risk factors for requiring a hysterectomy.Receiver operating characteristic curve analysis of the prediction model showed an area under the curve of 0.953(95%confidence interval:0.928-0.978)with a sensitivity of 90.50%and a specificity of 90.70%.CONCLUSION The model demonstrates excellent predictive power and is effective in guiding clinical decisions regarding PPH in the ethnic minority regions of Qiandongnan,China.

Uncertainty and disturbance estimator-based model predictive control for wet flue gas desulphurization system

Wet flue gas desulphurization technology is widely used in the industrial process for its capability of efficient pollution removal.The desulphurization control system,however,is subjected to complex reaction mechanisms and severe disturbances,which make for it difficult to achieve certain practically relevant control goals including emission and economic performances as well as system robustness.To address these challenges,a new robust control scheme based on uncertainty and disturbance estimator(UDE)and model predictive control(MPC)is proposed in this paper.The UDE is used to estimate and dynamically compensate acting disturbances,whereas MPC is deployed for optimal feedback regulation of the resultant dynamics.By viewing the system nonlinearities and unknown dynamics as disturbances,the proposed control framework allows to locally treat the considered nonlinear plant as a linear one.The obtained simulation results confirm that the utilization of UDE makes the tracking error negligibly small,even in the presence of unmodeled dynamics.In the conducted comparison study,the introduced control scheme outperforms both the standard MPC and PID(proportional-integral-derivative)control strategies in terms of transient performance and robustness.Furthermore,the results reveal that a lowpass-filter time constant has a significant effect on the robustness and the convergence range of the tracking error.

Predictive maintenance and its applications in civil engineering structures:A review

Structural health monitoring and performance prediction are crucial for smart disaster mitigation and intelligent management of structures throughout their lifespan.Recent advancements in predictive maintenance strategies within the industrial manufacturing industry have inspired similar innovations in civil engineering,aiming to improve structural performance evaluation,damage diagnosis,and capacity prediction.This review delves into the framework of predictive maintenance and examines various existing solutions,focusing on critical areas such as data acquisition,condition monitoring,damage prognosis,and maintenance planning.Results from real-world applications of predictive maintenance in civil engineering,covering high-rise structures,deep foundation pits,and other infrastructure,are presented.The challenges of implementing predictive maintenance in civil engineering structures under current technology,such as model interpretability of data-driven methods and standards for predictive maintenance,are explored.Future research prospects within this area are also discussed.

Finite-Time Stabilization for Constrained Discrete-time Systems by Using Model Predictive Control

In this paper, a model predictive control(MPC)framework is proposed for finite-time stabilization of linear and nonlinear discrete-time systems subject to state and control constraints. The proposed MPC framework guarantees the finite-time convergence property by assigning the control horizon equal to the dimension of the overall system, and only penalizing the terminal cost in the optimization, where the stage costs are not penalized explicitly. A terminal inequality constraint is added to guarantee the feasibility and stability of the closed-loop system.Initial feasibility can be improved via augmentation. The finite-time convergence of the proposed MPC is proved theoretically,and is supported by simulation examples.

Game Theory Based Model for Predictive Analytics Using Distributed Position Function

This paper presents a game theory-based method for predicting the outcomes of negotiation and group decision-making problems. We propose an extension to the BDM model to address problems where actors’ positions are distributed over a position spectrum. We generalize the concept of position in the model to incorporate continuous positions for the actors, enabling them to have more flexibility in defining their targets. We explore different possible functions to study the role of the position function and discuss appropriate distance measures for computing the distance between the positions of actors. To validate the proposed extension, we demonstrate the trustworthiness of our model’s performance and interpretation by replicating the results based on data used in earlier studies.

Three predictive scores compared in a retrospective multicenter study of nonunion tibial shaft fracture

BACKGROUND Delayed union,malunion,and nonunion are serious complications in the healing of fractures.Predicting the risk of nonunion before or after surgery is challenging.AIM To compare the most prevalent predictive scores of nonunion used in clinical practice to determine the most accurate score for predicting nonunion.METHODS We collected data from patients with tibial shaft fractures undergoing surgery from January 2016 to December 2020 in three different trauma hospitals.In this retrospective multicenter study,we considered only fractures treated with intramedullary nailing.We calculated the tibia FRACTure prediction healING days(FRACTING)score,Nonunion Risk Determination score,and Leeds-Genoa Nonunion Index(LEG-NUI)score at the time of definitive fixation.RESULTS Of the 130 patients enrolled,89(68.4%)healed within 9 months and were classified as union.The remaining patients(n=41,31.5%)healed after more than 9 months or underwent other surgical procedures and were classified as nonunion.After calculation of the three scores,LEG-NUI and FRACTING were the most accurate at predicting healing.CONCLUSION LEG-NUI and FRACTING showed the best performances by accurately predicting union and nonunion.

Developing and validating a predictive model of delivering large-forgestational-age infants among women with gestational diabetes mellitus

BACKGROUND The birth of large-for-gestational-age(LGA)infants is associated with many shortterm adverse pregnancy outcomes.It has been observed that the proportion of LGA infants born to pregnant women with gestational diabetes mellitus(GDM)is significantly higher than that born to healthy pregnant women.However,traditional methods for the diagnosis of LGA have limitations.Therefore,this study aims to establish a predictive model that can effectively identify women with GDM who are at risk of delivering LGA infants.AIM To develop and validate a nomogram prediction model of delivering LGA infants among pregnant women with GDM,and provide strategies for the effective prevention and timely intervention of LGA.METHODS The multivariable prediction model was developed by carrying out the following steps.First,the variables that were associated with LGA risk in pregnant women with GDM were screened by univariate analyses,for which the P value was<0.10.Subsequently,Least Absolute Shrinkage and Selection Operator regression was fit using ten cross-validations,and the optimal combination factors were se-lected by choosing lambda 1se as the criterion.The final predictors were deter-mined by multiple backward stepwise logistic regression analysis,in which only the independent variables were associated with LGA risk,with a P value<0.05.Finally,a risk prediction model was established and subsequently evaluated by using area under the receiver operating characteristic curve,calibration curve and decision curve analyses.RESULTS After using a multistep screening method,we establish a predictive model.Several risk factors for delivering an LGA infant were identified(P<0.01),including weight gain during pregnancy,parity,triglyceride-glucose index,free tetraiodothyronine level,abdominal circumference,alanine transaminase-aspartate aminotransferase ratio and weight at 24 gestational weeks.The nomogram’s prediction ability was supported by the area under the curve(0.703,0.709,and 0.699 for the training cohort,validation cohort,and test cohort,respectively).The calibration curves of the three cohorts displayed good agreement.The decision curve showed that the use of the 10%-60%threshold for identifying pregnant women with GDM who are at risk of delivering an LGA infant would result in a positive net benefit.CONCLUSION Our nomogram incorporated easily accessible risk factors,facilitating individualized prediction of pregnant women with GDM who are likely to deliver an LGA infant.

Establishment of predictive models and determinants of preoperative gastric retention in endoscopic retrograde cholangiopancreatography

BACKGROUND Study on influencing factors of gastric retention before endoscopic retrograde cholangiopancreatography(ERCP)background:With the wide application of ERCP,the risk of preoperative gastric retention affects the smooth progress of the operation.The study found that female,biliary and pancreatic malignant tumor,digestive tract obstruction and other factors are closely related to gastric retention,so the establishment of predictive model is very important to reduce the risk of operation.METHODS A retrospective analysis was conducted on 190 patients admitted to our hospital for ERCP preparation between January 2020 and February 2024.Patient baseline clinical data were collected using an electronic medical record system.Patients were randomly matched in a 1:4 ratio with data from 190 patients during the same period to establish a validation group(n=38)and a modeling group(n=152).Patients in the modeling group were divided into the gastric retention group(n=52)and non-gastric retention group(n=100)based on whether gastric retention occurred preoperatively.General data of patients in the validation group and identify factors influencing preoperative gastric retention in ERCP patients.A predictive model for preoperative gastric retention in ERCP patients was constructed,and calibration curves were used for validation.The receiver operating characteristic(ROC)curve was analyzed to evaluate the predictive value of the model.RESULTS We found no statistically significant difference in general data between the validation group and modeling group(P>0.05).The comparison of age,body mass index,hypertension,and diabetes between the two groups showed no statistically significant difference(P>0.05).However,we noted statistically significant differences in gender,primary disease,jaundice,opioid use,and gastrointestinal obstruction between the two groups(P<0.05).Mul-tivariate logistic regression analysis showed that gender,primary disease,jaundice,opioid use,and gastrointestinal obstruction were independent factors influencing preoperative gastric retention in ERCP patients(P<0.05).The results of logistic regression analysis revealed that gender,primary disease,jaundice,opioid use,and gastroin-testinal obstruction were included in the predictive model for preoperative gastric retention in ERCP patients.The calibration curves in the training set and validation set showed a slope close to 1,indicating good consistency between the predicted risk and actual risk.The ROC analysis results showed that the area under the curve(AUC)of the predictive model for preoperative gastric retention in ERCP patients in the training set was 0.901 with a standard error of 0.023(95%CI:0.8264-0.9567),and the optimal cutoff value was 0.71,with a sensitivity of 87.5 and specificity of 84.2.In the validation set,the AUC of the predictive model was 0.842 with a standard error of 0.013(95%CI:0.8061-0.9216),and the optimal cutoff value was 0.56,with a sensitivity of 56.2 and specificity of 100.0.CONCLUSION Gender,primary disease,jaundice,opioid use,and gastrointestinal obstruction are factors influencing preoperative gastric retention in ERCP patients.A predictive model established based on these factors has high predictive value.

Co-salient object detection with iterative purification and predictive optimization

Background Co-salient object detection(Co-SOD)aims to identify and segment commonly salient objects in a set of related images.However,most current Co-SOD methods encounter issues with the inclusion of irrelevant information in the co-representation.These issues hamper their ability to locate co-salient objects and significantly restrict the accuracy of detection.Methods To address this issue,this study introduces a novel Co-SOD method with iterative purification and predictive optimization(IPPO)comprising a common salient purification module(CSPM),predictive optimizing module(POM),and diminishing mixed enhancement block(DMEB).Results These components are designed to explore noise-free joint representations,assist the model in enhancing the quality of the final prediction results,and significantly improve the performance of the Co-SOD algorithm.Furthermore,through a comprehensive evaluation of IPPO and state-of-the-art algorithms focusing on the roles of CSPM,POM,and DMEB,our experiments confirmed that these components are pivotal in enhancing the performance of the model,substantiating the significant advancements of our method over existing benchmarks.Experiments on several challenging benchmark co-saliency datasets demonstrate that the proposed IPPO achieves state-of-the-art performance.